{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-fedml-ai--fedml","slug":"fedml-ai--fedml","name":"FedML","type":"platform","url":"https://TensorOpera.ai","page_url":"https://unfragile.ai/fedml-ai--fedml","categories":["model-training"],"tags":["ai-agent","deep-learning","distributed-training","edge-ai","federated-learning","inference-engine","machine-learning","mlops","model-deployment","model-serving","on-device-training"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-fedml-ai--fedml__cap_0","uri":"capability://automation.workflow.federated.learning.training.orchestration","name":"federated-learning-training-orchestration","description":"Orchestrates federated learning training across decentralized devices and servers using the Federated Averaging (FedAvg) algorithm, where model updates are aggregated server-side without exchanging raw data. Implements ServerAggregator and ClientTrainer interfaces with pluggable communication backends (MQTT, TRPC) to coordinate training rounds across heterogeneous edge devices, mobile phones, and cloud servers. Supports both synchronous and asynchronous aggregation patterns with configurable convergence criteria.","intents":["Train ML models across multiple organizations without sharing raw data","Deploy federated learning across edge devices and mobile phones while maintaining privacy","Simulate federated learning scenarios for research and algorithm validation","Coordinate model training across geographically distributed data silos"],"best_for":["Research teams validating federated learning algorithms","Healthcare and financial institutions training models on sensitive data","IoT and edge computing platforms requiring on-device training","Teams building privacy-preserving ML systems across organizational boundaries"],"limitations":["Communication overhead scales with number of clients — synchronous aggregation blocks on slowest client","Convergence may be slower than centralized training due to data heterogeneity across clients","Requires stable network connectivity for client-server communication; no built-in offline-first training","FedAvg algorithm assumes IID data distribution — performance degrades significantly with non-IID data"],"requires":["Python 3.7+","PyTorch or TensorFlow installed","MQTT broker (e.g., Mosquitto) or TRPC server for communication","Network connectivity between server and all client devices"],"input_types":["model architecture (PyTorch/TensorFlow)","local training datasets on each client","hyperparameter configuration (learning rate, rounds, local epochs)"],"output_types":["aggregated model weights","training metrics (loss, accuracy per round)","convergence logs and client participation records"],"categories":["automation-workflow","distributed-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-fedml-ai--fedml__cap_1","uri":"capability://automation.workflow.cross.cloud.job.scheduling.and.launch","name":"cross-cloud-job-scheduling-and-launch","description":"FedML Launch provides a unified scheduler that abstracts away cloud provider differences, enabling users to submit ML jobs once and execute them across AWS, Azure, GCP, or on-premise clusters without code changes. The Scheduler Layer manages resource allocation, job distribution, and execution environment provisioning by translating job specifications into provider-specific configurations. Integrates with Docker for containerized deployment and supports both batch and interactive job modes.","intents":["Run the same training job across multiple cloud providers to optimize cost","Execute ML workloads on on-premise GPU clusters without rewriting deployment code","Automatically provision and tear down cloud resources based on job requirements","Migrate ML jobs between cloud providers without application code changes"],"best_for":["MLOps teams managing multi-cloud infrastructure","Researchers comparing performance across cloud providers","Enterprises with hybrid cloud and on-premise deployments","Cost-conscious teams optimizing cloud spending across providers"],"limitations":["Scheduler abstraction adds latency to job startup — typically 30-60 seconds for cloud resource provisioning","Provider-specific features (e.g., spot instances, custom networking) may not be fully exposed through abstraction layer","Requires Docker containerization — not suitable for workflows requiring bare-metal performance tuning","On-premise cluster integration requires manual setup of FedML agent on each node"],"requires":["Python 3.7+","Docker installed and running","Cloud provider credentials (AWS, Azure, GCP) or on-premise cluster access","FedML CLI installed (pip install fedml)","Network connectivity to target cloud providers or on-premise clusters"],"input_types":["job specification (YAML or Python config)","Docker image URI or Dockerfile","resource requirements (CPU, GPU, memory)","cloud provider credentials"],"output_types":["job execution logs","resource utilization metrics","job status and completion reports","cost breakdown by cloud provider"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-fedml-ai--fedml__cap_10","uri":"capability://automation.workflow.docker.containerization.and.deployment","name":"docker-containerization-and-deployment","description":"Integrates Docker containerization for packaging training and serving workloads with automatic image building from source code. Provides Docker deployment templates for common ML scenarios (distributed training, federated learning, model serving) that can be customized via configuration. Supports multi-stage builds for optimized image sizes and layer caching for faster iteration.","intents":["Package training code and dependencies into reproducible Docker images","Deploy ML workloads consistently across different cloud providers and on-premise clusters","Simplify dependency management and environment setup for distributed training","Enable CI/CD pipelines to automatically build and push Docker images"],"best_for":["Teams deploying ML workloads to Kubernetes or container orchestration platforms","Organizations requiring reproducible and portable training environments","CI/CD pipelines automating model training and deployment","Multi-cloud deployments requiring consistent containerized workloads"],"limitations":["Docker image size can be large (2-5GB) for ML frameworks — increases deployment time","Container overhead (memory, CPU) is non-negligible for latency-sensitive inference","Debugging inside containers is more difficult than local development","GPU support requires NVIDIA Docker runtime and proper driver configuration"],"requires":["Docker installed and running","Docker Hub or private container registry for image storage","Dockerfile or FedML Docker template","NVIDIA Docker runtime for GPU support"],"input_types":["training code and dependencies","Docker template or custom Dockerfile","configuration files for training/serving","base image selection (Python, CUDA, etc.)"],"output_types":["Docker image URI","image metadata (size, layers, build time)","deployment manifests (Kubernetes YAML, Docker Compose)","build logs and optimization recommendations"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-fedml-ai--fedml__cap_11","uri":"capability://data.processing.analysis.runtime.logging.and.event.tracking","name":"runtime-logging-and-event-tracking","description":"Implements MLOpsRuntimeLogDaemon for asynchronous event logging during training and inference, capturing training events, system events, and errors without blocking execution. Provides structured event format (MLOpsProfilerEvent) with timestamps and metadata for post-hoc analysis. Supports log rotation and compression to manage disk space for long-running jobs.","intents":["Capture detailed training events and system logs for debugging and analysis","Track training progress and detect anomalies through event logs","Generate audit trails for compliance and reproducibility","Analyze training dynamics and identify convergence issues post-hoc"],"best_for":["Teams debugging training failures and performance issues","Organizations requiring audit trails for compliance","Research teams analyzing training dynamics and convergence behavior","Long-running training jobs requiring event tracking"],"limitations":["Asynchronous logging may lose events if process crashes before flush","Log file size can grow large (GBs) for long-running jobs — requires log rotation","Structured logging adds overhead compared to simple print statements","Log analysis requires parsing and aggregation tools for large-scale jobs"],"requires":["Python 3.7+","Disk space for log files (typically 1-10GB for long-running jobs)","Log rotation and compression tools (logrotate, gzip)"],"input_types":["training events (epoch start/end, loss values, etc.)","system events (GPU memory, network bandwidth, etc.)","error and exception information","custom application events"],"output_types":["structured event logs with timestamps","compressed log archives","event statistics and summaries","anomaly detection alerts"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-fedml-ai--fedml__cap_12","uri":"capability://planning.reasoning.algorithm.framework.and.extensibility","name":"algorithm-framework-and-extensibility","description":"Provides pluggable algorithm framework with ServerAggregator and ClientTrainer interfaces enabling implementation of custom federated learning algorithms beyond FedAvg. Supports algorithm composition and chaining for complex training pipelines. Includes reference implementations (FedAvgAggregator, FedAvgTrainer) demonstrating interface contracts and best practices.","intents":["Implement custom federated learning algorithms (FedProx, FedAdam, etc.) without modifying core framework","Compose multiple algorithms for advanced training scenarios","Validate new federated learning algorithms through simulation before deployment","Share algorithm implementations across research community"],"best_for":["Research teams developing novel federated learning algorithms","Organizations requiring custom aggregation strategies for specific data distributions","Teams combining multiple algorithms for advanced training scenarios","ML practitioners extending FedML with proprietary algorithms"],"limitations":["Custom algorithm implementation requires understanding ServerAggregator/ClientTrainer interfaces","Algorithm composition adds complexity and potential for subtle bugs","Performance optimization for custom algorithms requires profiling and tuning","Limited documentation for advanced algorithm development"],"requires":["Python 3.7+","PyTorch or TensorFlow for model training","Understanding of federated learning algorithm design","FedML library with algorithm framework"],"input_types":["custom ServerAggregator implementation","custom ClientTrainer implementation","algorithm hyperparameters and configuration","training data and model architecture"],"output_types":["aggregated model weights","training metrics and convergence analysis","algorithm performance comparison","implementation reference for community"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-fedml-ai--fedml__cap_13","uri":"capability://planning.reasoning.multi.platform.cross.device.training.simulation","name":"multi-platform-cross-device-training-simulation","description":"Provides simulation environment for federated learning across heterogeneous devices (servers, edge devices, mobile phones) without requiring actual hardware deployment. Simulates network latency, device failures, and data heterogeneity to validate algorithm behavior before production deployment. Supports both synchronous and asynchronous simulation modes with configurable device characteristics.","intents":["Test federated learning algorithms on simulated device networks before real deployment","Evaluate impact of network latency and device failures on training convergence","Study effects of data heterogeneity across devices on algorithm performance","Validate system scalability to thousands of devices without actual hardware"],"best_for":["Research teams validating federated learning algorithms","Organizations planning large-scale federated deployments","Teams studying impact of network conditions on training","ML practitioners prototyping federated learning systems"],"limitations":["Simulation may not capture all real-world complexities (e.g., device heterogeneity, network variability)","Computational cost of simulating thousands of devices can be high (hours to days)","Simulation results may not perfectly predict real-world performance","Requires careful tuning of simulation parameters to match real-world conditions"],"requires":["Python 3.7+","PyTorch or TensorFlow for model training","FedML library with simulation framework","Sufficient computational resources (multi-core CPU or GPU)"],"input_types":["federated learning algorithm configuration","device characteristics (CPU, memory, network bandwidth)","network conditions (latency, packet loss, bandwidth)","data distribution across devices","failure scenarios (device dropout, network partitions)"],"output_types":["training metrics (loss, accuracy per round)","convergence analysis and comparison","impact analysis of network conditions and failures","scalability projections for real deployment"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-fedml-ai--fedml__cap_2","uri":"capability://automation.workflow.distributed.model.training.with.data.parallelism","name":"distributed-model-training-with-data-parallelism","description":"Enables large-scale distributed training of foundational models using data parallelism across multiple GPUs and nodes. Implements gradient synchronization and model parameter averaging using AllReduce collective operations, with support for mixed-precision training and gradient accumulation. Integrates with PyTorch DistributedDataParallel and TensorFlow distributed strategies to transparently distribute training across heterogeneous hardware while maintaining single-machine code semantics.","intents":["Train large language models and vision models across multi-GPU clusters","Reduce training time for large datasets by distributing data across multiple nodes","Scale training from single GPU to hundreds of GPUs without rewriting training loops","Optimize GPU utilization and reduce per-sample training cost"],"best_for":["ML teams training large foundational models (LLMs, vision transformers)","Research labs with access to multi-GPU clusters","Organizations optimizing training cost and time for large datasets","Teams requiring reproducible distributed training across different hardware"],"limitations":["Communication overhead (AllReduce) becomes bottleneck beyond 64-128 GPUs without high-bandwidth interconnect","Requires careful tuning of batch size, learning rate, and synchronization frequency for convergence","Data heterogeneity across nodes can lead to stale gradient issues in asynchronous training","Not suitable for models with dynamic computation graphs or irregular memory access patterns"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","NVIDIA CUDA 11.0+ and cuDNN 8.0+ for GPU training","High-speed interconnect (InfiniBand or 10Gbps Ethernet) for multi-node training","NCCL 2.8+ for GPU collective communication"],"input_types":["model definition (PyTorch nn.Module or TensorFlow Keras model)","training dataset (distributed across nodes)","hyperparameters (batch size, learning rate, number of epochs)","hardware configuration (number of GPUs, nodes)"],"output_types":["trained model weights","training metrics (loss, accuracy, throughput)","performance profiling data (communication time, computation time)","checkpoints for fault recovery"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-fedml-ai--fedml__cap_3","uri":"capability://automation.workflow.model.serving.and.inference.deployment","name":"model-serving-and-inference-deployment","description":"Provides high-performance model serving infrastructure for scalable inference across cloud and edge environments. Implements model loading, batching, and request routing with support for multiple model formats (ONNX, TorchScript, SavedModel). Integrates with containerization and auto-scaling to handle variable inference loads, with built-in monitoring for latency and throughput metrics.","intents":["Deploy trained models to production with automatic scaling based on request volume","Serve multiple model versions simultaneously for A/B testing and gradual rollouts","Achieve low-latency inference on edge devices and cloud servers","Monitor model performance and detect inference degradation in production"],"best_for":["ML teams deploying models to production APIs","Organizations requiring multi-model serving with version management","Edge computing platforms needing efficient on-device inference","Teams building real-time inference systems with strict latency requirements"],"limitations":["Batching introduces latency trade-off — optimal batch size depends on model and hardware","Model format conversion (e.g., PyTorch to ONNX) may lose precision or unsupported operations","Auto-scaling has cold-start latency (30-60 seconds) when provisioning new instances","No built-in A/B testing framework — requires external traffic splitting logic"],"requires":["Python 3.7+","Model in supported format (ONNX, TorchScript, SavedModel, or FedML native format)","Docker for containerized deployment","Kubernetes or cloud provider container orchestration for auto-scaling","Monitoring infrastructure (Prometheus, CloudWatch, etc.) for metrics collection"],"input_types":["trained model weights and architecture","inference request data (images, text, structured data)","serving configuration (batch size, timeout, resource limits)","model metadata (input/output schemas, preprocessing requirements)"],"output_types":["inference predictions","confidence scores or probabilities","latency and throughput metrics","model performance logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-fedml-ai--fedml__cap_4","uri":"capability://safety.moderation.privacy.preserving.defense.mechanisms","name":"privacy-preserving-defense-mechanisms","description":"Implements FedMLDefender component with multiple defense mechanisms against adversarial attacks in federated learning, including differential privacy, robust aggregation, and anomaly detection. Provides configurable privacy budgets and defense strategies that can be applied transparently to training pipelines without modifying algorithm code. Integrates with attack simulation framework for testing defense effectiveness.","intents":["Protect federated learning systems from poisoning attacks by malicious clients","Add differential privacy guarantees to federated training with configurable privacy budgets","Detect and mitigate model inversion attacks that attempt to reconstruct training data","Validate defense mechanisms through adversarial attack simulation before deployment"],"best_for":["Healthcare and financial institutions handling sensitive data","Federated learning systems with untrusted or potentially compromised clients","Research teams studying adversarial robustness in federated settings","Compliance-driven organizations requiring formal privacy guarantees (GDPR, HIPAA)"],"limitations":["Differential privacy adds noise that degrades model accuracy — privacy-utility trade-off must be tuned per application","Robust aggregation (e.g., median, trimmed mean) reduces model quality compared to standard averaging","Anomaly detection has false positive rate — may reject legitimate clients with unusual data distributions","Defense mechanisms add computational overhead (10-30% per training round) for privacy budget tracking and noise injection"],"requires":["Python 3.7+","PyTorch or TensorFlow for model training","Cryptography library for secure aggregation (if using encrypted aggregation)","Understanding of privacy-utility trade-offs and appropriate epsilon values for differential privacy"],"input_types":["federated learning training configuration","defense strategy selection (differential privacy, robust aggregation, anomaly detection)","privacy budget parameters (epsilon, delta for DP)","attack simulation parameters (attack type, attack strength)"],"output_types":["privacy-protected model weights","privacy budget consumption logs","defense effectiveness metrics (attack success rate, model accuracy)","anomaly detection alerts and rejected client reports"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-fedml-ai--fedml__cap_5","uri":"capability://safety.moderation.attack.simulation.and.adversarial.testing","name":"attack-simulation-and-adversarial-testing","description":"Implements FedMLAttacker component that simulates various adversarial attacks (poisoning, model inversion, membership inference) against federated learning systems to validate defense mechanisms. Provides configurable attack strategies and intensity levels that can be injected into training pipelines for red-teaming and robustness validation. Generates detailed attack success metrics and vulnerability reports.","intents":["Test federated learning systems for vulnerability to poisoning attacks before production deployment","Validate effectiveness of privacy and security defenses through adversarial simulation","Identify weak points in federated learning pipelines through systematic attack scenarios","Generate security audit reports demonstrating defense robustness"],"best_for":["Security teams conducting red-team testing of federated learning systems","Research teams studying adversarial robustness in federated settings","Organizations requiring security compliance and audit trails","ML teams validating defense mechanisms before production deployment"],"limitations":["Attack simulation may not capture all real-world attack vectors or sophisticated adversaries","Computational cost of running attacks can be significant (2-5x training time for comprehensive testing)","Some attacks require assumptions about attacker capabilities (e.g., model inversion assumes white-box access)","Attack success metrics are heuristic-based and may not perfectly correlate with real-world impact"],"requires":["Python 3.7+","PyTorch or TensorFlow for model training","FedML library with attack simulation module","Understanding of attack types and threat models being tested"],"input_types":["federated learning training configuration","attack type selection (poisoning, model inversion, membership inference, etc.)","attack parameters (attack strength, number of malicious clients, attack timing)","target model and training data"],"output_types":["attack success metrics (accuracy degradation, data reconstruction quality)","vulnerability reports with attack details","defense effectiveness analysis","recommendations for improving robustness"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-fedml-ai--fedml__cap_6","uri":"capability://tool.use.integration.mqtt.and.s3.communication.integration","name":"mqtt-and-s3-communication-integration","description":"Implements MqttCommManager and S3 integration for reliable message-oriented communication between federated learning clients and servers, with support for asynchronous message queuing and cloud storage for model checkpoints. Uses MQTT publish-subscribe pattern for decoupled client-server communication, enabling clients to connect/disconnect without blocking aggregation. Integrates with S3-compatible storage for distributed model versioning and checkpoint management.","intents":["Enable federated learning across unreliable networks with asynchronous message queuing","Support client devices that connect intermittently without blocking server aggregation","Store and version model checkpoints in cloud storage for fault recovery","Decouple client and server components for independent scaling and maintenance"],"best_for":["Federated learning systems with mobile or edge clients on unreliable networks","Deployments requiring asynchronous communication and eventual consistency","Organizations using AWS S3 or S3-compatible storage (MinIO, DigitalOcean Spaces)","Systems requiring checkpoint management and model versioning"],"limitations":["MQTT message ordering guarantees are per-client only — global ordering requires application-level logic","S3 consistency model is eventual — recent writes may not be immediately visible","MQTT broker becomes single point of failure without clustering/replication setup","S3 API calls add latency (100-500ms) compared to local filesystem storage"],"requires":["Python 3.7+","MQTT broker (Mosquitto, HiveMQ, or cloud-hosted) with network accessibility","AWS S3 account or S3-compatible storage service","paho-mqtt Python library for MQTT communication","boto3 library for S3 integration"],"input_types":["MQTT broker connection parameters (host, port, credentials)","S3 bucket configuration (bucket name, region, credentials)","model updates and gradients for transmission","checkpoint metadata (model version, timestamp, training round)"],"output_types":["transmitted model updates and aggregated weights","S3 checkpoint URIs and version history","message delivery confirmation and latency metrics","communication logs and error reports"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-fedml-ai--fedml__cap_7","uri":"capability://automation.workflow.android.sdk.and.mobile.device.training","name":"android-sdk-and-mobile-device-training","description":"Provides Android SDK enabling federated learning training directly on mobile devices with on-device model updates and gradient computation. Implements lightweight ClientTrainer for Android that communicates with federated learning servers via MQTT or HTTP, with support for model quantization and compression to fit memory constraints. Handles battery and network state management to pause/resume training based on device conditions.","intents":["Train ML models on mobile devices without uploading raw user data to servers","Leverage billions of mobile devices as distributed training nodes for federated learning","Build privacy-preserving mobile applications with on-device model personalization","Reduce server-side computational burden by distributing training to edge devices"],"best_for":["Mobile app developers building privacy-preserving ML features","Organizations training models on user data without data collection","Large-scale federated learning systems leveraging mobile device networks","Applications requiring on-device personalization (keyboard, recommendation systems)"],"limitations":["Mobile device computational power is limited — training only feasible for small to medium models","Battery and network constraints require careful scheduling — training may be interrupted","Model quantization (int8, fp16) necessary for mobile memory constraints introduces accuracy loss","Debugging and monitoring on-device training is difficult compared to server-side training","Requires Android 8.0+ and minimum 2GB RAM for practical training"],"requires":["Android SDK 26+ (Android 8.0+)","Android Studio for development","FedML Android SDK library","PyTorch Mobile or TensorFlow Lite for on-device inference/training","Minimum 2GB RAM on target devices"],"input_types":["model architecture (quantized PyTorch or TensorFlow Lite format)","local training data on device","hyperparameters (local epochs, batch size, learning rate)","communication configuration (server address, MQTT broker)"],"output_types":["model gradients or weight updates","training metrics (loss, accuracy on local data)","device telemetry (battery level, network state, training time)","participation logs for server-side aggregation"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-fedml-ai--fedml__cap_8","uri":"capability://data.processing.analysis.mlops.metrics.collection.and.profiling","name":"mlops-metrics-collection-and-profiling","description":"Implements MLOps metrics collection system (MLOpsMetrics, MLOpsProfilerEvent) that captures training performance data including loss, accuracy, throughput, communication time, and resource utilization. Provides runtime logging daemon (MLOpsRuntimeLogDaemon) that asynchronously collects metrics without blocking training, with integration to cloud monitoring platforms. Enables performance profiling and bottleneck identification across distributed training jobs.","intents":["Monitor training progress and detect convergence issues in real-time","Identify performance bottlenecks (communication vs computation) in distributed training","Profile resource utilization (CPU, GPU, memory, network) across training nodes","Generate performance reports for training optimization and cost analysis"],"best_for":["ML teams optimizing distributed training performance","Organizations monitoring production training jobs for anomalies","Research teams analyzing communication overhead in federated learning","Teams requiring detailed performance profiling for cost optimization"],"limitations":["Metrics collection adds 5-10% overhead to training throughput","Asynchronous logging may lose metrics if process crashes before flush","High-frequency metric collection (per-batch) can overwhelm monitoring backends","Profiling granularity trade-off — fine-grained profiling increases overhead"],"requires":["Python 3.7+","PyTorch or TensorFlow for training","Monitoring backend (Prometheus, CloudWatch, Wandb, etc.)","Network connectivity to monitoring platform"],"input_types":["training configuration and hyperparameters","monitoring backend credentials and endpoints","metric collection frequency and granularity settings","profiling event definitions"],"output_types":["training metrics (loss, accuracy, throughput)","performance profiling data (communication time, computation time, memory usage)","resource utilization metrics (CPU, GPU, network bandwidth)","performance reports and bottleneck analysis"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-fedml-ai--fedml__cap_9","uri":"capability://tool.use.integration.cli.and.configuration.management","name":"cli-and-configuration-management","description":"Provides command-line interface (CLI) for job submission, model deployment, and system management with configuration file support (YAML/JSON). Implements MLOpsConfigs for centralized configuration management across training, serving, and federated learning components. Supports environment variable substitution and configuration inheritance for managing complex multi-environment deployments.","intents":["Submit training and inference jobs from command line without writing Python code","Manage configurations across development, staging, and production environments","Automate job submission and monitoring through shell scripts and CI/CD pipelines","Version control training configurations alongside code"],"best_for":["MLOps engineers managing training and deployment pipelines","Teams integrating FedML into CI/CD workflows","Organizations requiring reproducible job configurations","Users preferring CLI over programmatic APIs"],"limitations":["CLI abstractions may not expose all advanced configuration options","Configuration file format (YAML/JSON) has limited expressiveness compared to Python code","Error messages from CLI may be less detailed than programmatic API errors","CLI version compatibility issues when upgrading FedML across major versions"],"requires":["Python 3.7+","FedML CLI installed (pip install fedml)","Configuration file in YAML or JSON format","Appropriate credentials for cloud providers or on-premise clusters"],"input_types":["configuration files (YAML/JSON)","command-line arguments","environment variables","job specifications"],"output_types":["job submission confirmation","job status and logs","configuration validation results","deployment reports"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch or TensorFlow installed","MQTT broker (e.g., Mosquitto) or TRPC server for communication","Network connectivity between server and all client devices","Docker installed and running","Cloud provider credentials (AWS, Azure, GCP) or on-premise cluster access","FedML CLI installed (pip install fedml)","Network connectivity to target cloud providers or on-premise clusters","Docker Hub or private container registry for image storage","Dockerfile or FedML Docker template"],"failure_modes":["Communication overhead scales with number of clients — synchronous aggregation blocks on slowest client","Convergence may be slower than centralized training due to data heterogeneity across clients","Requires stable network connectivity for client-server communication; no built-in offline-first training","FedAvg algorithm assumes IID data distribution — performance degrades significantly with non-IID data","Scheduler abstraction adds latency to job startup — typically 30-60 seconds for cloud resource provisioning","Provider-specific features (e.g., spot instances, custom networking) may not be fully exposed through abstraction layer","Requires Docker containerization — not suitable for workflows requiring bare-metal performance tuning","On-premise cluster integration requires manual setup of FedML agent on each node","Docker image size can be large (2-5GB) for ML frameworks — increases deployment time","Container overhead (memory, CPU) is non-negligible for latency-sensitive inference","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.30293250008545997,"quality":0.6,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-04-22T08:01:53.258Z","last_commit":"2025-10-28T12:44:12Z"},"community":{"stars":4035,"forks":767,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=fedml-ai--fedml","compare_url":"https://unfragile.ai/compare?artifact=fedml-ai--fedml"}},"signature":"QKpmkaJtTFfCSnqbzh9GPPSeJyzskN6tY4A8PURmCij0tf53IySeX3xhD/6IvrwZwhBQHYZOOwgEM+HgBtxCBQ==","signedAt":"2026-06-19T19:30:05.224Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/fedml-ai--fedml","artifact":"https://unfragile.ai/fedml-ai--fedml","verify":"https://unfragile.ai/api/v1/verify?slug=fedml-ai--fedml","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}